Systems and Methods for Automated Staging and Capture of Real Estate Negotiations

1. A client device for intelligently staging negotiations, comprising: a memory configured to store non-transitory computer readable instructions; and a processor communicatively coupled to the memory, wherein the processor, when executing the non-transitory computer readable instructions, is configured to: receive, from a server, a copy of a machine-learning model trained on a dataset of preferences from a first party and a second party, wherein the machine-learning model is deployed on the server for use by the client device when the client device is connected to the server, and wherein the dataset of preferences comprises a transaction history related to the first party and a transaction history related to the second party; configure the copy of the machine-learning model for use by the client device when the client device lacks a connection to the server; cache the copy of the machine-learning model locally on the client device; receive a first proposal from the first party; lock the first proposal; generate a clone of the first proposal, wherein the clone of the first proposal is editable by the second party; receive at least one edit to the clone of the first proposal from the second party; receive current market data related to the first proposal; input the at least one edit and the current market data related to the first proposal to the copy of the machine-learning model cached on the client device when the client device lacks the connection to the server; compare the at least one edit and the current market data to the dataset of preferences from the first party and the second party; based on comparing the at least one edit and the current market data to the dataset, update the machine-learning model deployed on the server when the client device is connected to the server; based on the update of the machine-learning model on the server, generate at least one counter-proposal suggestion; receive an approval indication of the at least one counter-proposal suggestion, wherein the approval indication transforms the at least one counter-proposal suggestion into the counter-proposal; and transmit the counter-proposal to the first party.

2. The client device of claim 1, wherein the first proposal from the first party is related to at least one property characteristic.

3. The client device of claim 2, wherein the processor is further configured to analyze data related to the at least one property characteristic, wherein the at least one property characteristic comprises at least one of: a geographic location, a size, a price, a term, a duration, a distance from at least one point-of-interest, and a commencement date.

4. The client device of claim 3, wherein the data related to the at least one property characteristic comprises comparison data from at least one similarly-situated property location, wherein the comparison data comprises at least one of: a geographic location, a size, a price, a term duration, a rent concession, a distance form at least one point-of-interest, and facilities information.

5. The client device of claim 1, wherein the processor is further configured to receive at least one edit to the at least one counter-proposal suggestion.

6. The client device of claim 5, wherein the processor is further configured to generate a second counter-proposal suggestion based on the at least one edit to the at least one counter-proposal suggestion.

7. The client device of claim 1, wherein the processor is further configured to lock the counter-proposal, wherein the locked version is configured for transmission to at least one of: the second party and a third party.

8. The client device of claim 7, wherein the locked version of the at least one counter-proposal is provided to the first party simultaneously with at least one cloned version of the at least one counter-proposal, wherein the at least one cloned version is editable.

9. The client device of claim 1, wherein the processor is further configured to: receive a plurality of proposals; analyze the plurality of proposals, wherein analyzing the plurality of proposals comprises comparing the plurality of proposals with at least one preference associated with the second party; and rank the plurality of proposals based on the at least one preference associated with the second party.

10. The client device of claim 1, wherein the transaction history related to the first party and the transaction history related to the second party comprises at least one negotiation metric.

11. The client device of claim 1, wherein the processor is further configured to display at least one change indicator, wherein the at least one change indicator indicates a change between the first proposal and the at least one counter-proposal.

12. A computer-readable media storing non-transitory computer executable instructions that when executed cause a computing system to perform a method for intelligently staging negotiations comprising: receiving, from a server, a copy of a machine-learning model trained on a dataset of preferences from a first party and a second party, wherein the machine-learning model is deployed on the server for use by a client device when the client device is connected to the server, and wherein the dataset of preferences comprises a transaction history related to the first party and a transaction history related to the second party; configuring the copy of the machine-learning model for use by the client device when the client device lacks a connection to the server; caching the copy of the machine-learning model locally on the client device; receiving a proposal from the first party, wherein the proposal is related to at least one property location; locking the proposal from the first party; generating a clone of the proposal, wherein the clone of the proposal is editable by the second party; receiving at least one edit to the clone of the proposal from the second party; receiving current market data related to the proposal; inputting the at least one edit and the current market data related to the proposal to the copy of the machine-learning model cached on the client device when the client device lacks the connection to the server; comparing the at least one edit and the current market data to the dataset of preferences from the first party and the second party; based on comparing the at least one edit and the current market data to the dataset, updating the machine-learning model deployed on the server when the client device is connected to the server; based on the update to the machine-learning model on the server, generating at least one proposal suggestion; transforming the at least one proposal suggestion into the counter-proposal; and providing the counter-proposal to the first party.

13. The computer-readable media of claim 12, wherein the first proposal from the first party is related to at least one property characteristic.

14. The computer-readable media of claim 13, wherein the method comprises: analyzing data related to the at least one property characteristic, wherein the at least one property characteristic comprises at least one of: a geographic location, a size, a price, a term, a duration, a distance from at least one point-of-interest, and a commencement date.

15. The computer-readable media of claim 14, wherein the data related to the at least one property characteristic comprises comparison data from at least one similarly-situated property location, wherein the comparison data comprises at least one of: a geographic location, a size, a price, a term duration, a rent concession, a distance form at least one point-of-interest, and facilities information.

16. The computer-readable media of claim 12, wherein the method comprises: receiving at least one edit to the at least one counter-proposal suggestion.

17. The computer-readable media of claim 16, wherein the method comprises: generating a second counter-proposal suggestion based on the at least one edit to the at least one counter-proposal suggestion.

18. The computer-readable media of claim 12, wherein the method comprises: locking the counter-proposal, wherein the locked version is configured for transmission to at least one of: the second party and a third party.

19. The computer-readable media of claim 18, wherein the locked version of the counter-proposal is provided to the first party simultaneously with at least one cloned version of the counter-proposal, wherein the at least one cloned version is editable.

20. The computer-readable media of claim 12, wherein the method comprises: receiving a plurality of proposals; analyzing the plurality of proposals, wherein analyzing the plurality of proposals comprises comparing the plurality of proposals with at least one preference associated with the second party; and ranking the plurality of proposals based on the at least one preference associated with the second party.